Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer
中文摘要
RAG上下文工程通过四个类型化输入优化LLM调用，后续将扩展至语料库、对话及工具。
English Summary
Context Engineering for RAG uses four typed inputs to optimize LLM calls, with future expansions into corpus, conversation, and tools.
Original Excerpt
Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on one LLM call. Corpus, conversation, and tool extensions are follow-up work The post Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer appeared first on Towards Data Science.